Tata Alessandra, Massaro Andrea, Marzoli Filippo, Miano Brunella, Bragolusi Marco, Piro Roberto, Belluco Simone
Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico Sperimentale delle Venezie, 36100 Vicenza, Italy.
Department of Food Safety, Istituto Zooprofilattico Sperimentale delle Venezie, 35020 Legnaro, Italy.
Foods. 2022 Jul 29;11(15):2264. doi: 10.3390/foods11152264.
This feasibility study reports the use of direct analysis in real-time high-resolution mass spectrometry (DART-HRMS) in profiling the powders from edible insects, as well as the potential for the identification of different insect species by classification modeling. The basis of this study is the revolution that has occurred in the field of analytical chemistry, with the improved capability of ambient mass spectrometry to authenticate food matrices. In this study, we applied DART-HRMS, coupled with mid-level data fusion and a learning method, to discriminate between (house cricket), (yellow mealworm), (migratory locust), and (silk moth). A distinct metabolic fingerprint was observed for each edible insect species, while the fingerprint was characterized by highly abundant linolenic acid and quinic acid; palmitic and oleic acids are the statistically predominant fatty acids in black soldier fly (). Our chemometrics also revealed that the amino acid proline is a discriminant molecule in , whereas palmitic and linoleic acids are the most informative molecular features of the house cricket (). Good separation between the four different insect species was achieved, and cross-validation gave 100% correct identification for all training samples. The performance of the random forest classifier was examined on a test set and produced excellent results, in terms of overall accuracy, sensitivity, and specificity. These results demonstrate the reliability of the DART-HRMS as a screening method in a future quality control scenario to detect complete substitution of insect powders.
本可行性研究报告了实时高分辨率质谱直接分析(DART-HRMS)在分析食用昆虫粉末中的应用,以及通过分类建模识别不同昆虫种类的潜力。本研究的基础是分析化学领域发生的变革,即常压质谱鉴定食品基质的能力有所提高。在本研究中,我们应用DART-HRMS,并结合中级数据融合和一种学习方法,来区分家蟋蟀、黄粉虫、飞蝗和家蚕。每种食用昆虫都观察到独特的代谢指纹图谱,而家蟋蟀的指纹图谱以高含量的亚麻酸和奎尼酸为特征;棕榈酸和油酸是黑水虻中统计学上占主导地位的脂肪酸。我们的化学计量学还表明,氨基酸脯氨酸是家蟋蟀中的判别分子,而棕榈酸和亚油酸是家蟋蟀中最具信息性的分子特征。四种不同昆虫之间实现了良好的分离,交叉验证对所有训练样本的正确识别率为100%。在测试集上检验了随机森林分类器的性能,在总体准确性、敏感性和特异性方面都产生了优异的结果。这些结果证明了DART-HRMS作为一种筛选方法在未来质量控制场景中检测昆虫粉末完全替代情况的可靠性。